AmericaView 2023

Interactive Cloud Computing with Google Earth Engine and Geemap

Introduction: This is a notebook prepared for the workshop at the AmericaView Annual Conference at Lafayette, Louisiana on March 13, 2023.

Overview: Google Earth Engine (GEE) is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. It enables scientists, researchers, and developers to analyze and visualize changes on the Earth’s surface. The geemap Python package provides GEE users with an intuitive interface to manipulate, analyze, and visualize geospatial big data interactively in a Jupyter-based environment. The topics will be covered in this workshop include: (1) introducing geemap and the Earth Engine Python API; (2) creating interactive maps; (3) searching GEE data catalog; (4) visualizing GEE datasets; (5) analyzing GEE datasets, and (6) exporting GEE datasets. More information about the geemap Python package can be found at https://geemap.org.

Requirement: Please sign up for a Google Earth Engine account if you don’t have one yet. No software installation is needed. You just need a browser with Internet access for this workshop.

Why Earth Engine Python API and geemap?¶

Check out the slides here.

Install packages¶

In [ ]:

# %pip install geemap

# %pip install geemap

Import libraries¶

In [ ]:

import ee
import geemap.foliumap as geemap

import ee import geemap.foliumap as geemap

Authenticate Earth Engine¶

You will need to create a Google Cloud Project and enable the Earth Engine API for the project. You can find detailed instructions here.

In [ ]:

geemap.ee_initialize()

geemap.ee_initialize()

Create interactive maps¶

Let's create an interactive map using the ipyleaflet plotting backend. The geemap.Map class inherits from the ipyleaflet.Map class. Therefore, you can use the same syntax to create an interactive map as you would with ipyleaflet.Map.

In [ ]:

Map = geemap.Map()

Map = geemap.Map()

To display it in a Jupyter notebook, simply ask for the object representation:

In [ ]:

Map

Map

To customize the map, you can specify various keyword arguments, such as center ([lat, lon]), zoom, width, and height. The default width is 100%, which takes up the entire cell width of the Jupyter notebook. The height argument accepts a number or a string. If a number is provided, it represents the height of the map in pixels. If a string is provided, the string must be in the format of a number followed by px, e.g., 600px.

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4, height=600)
Map

Map = geemap.Map(center=[40, -100], zoom=4, height=600) Map

To hide a control, set control_name to False, e.g., draw_ctrl=False.

In [ ]:

Map = geemap.Map(data_ctrl=False, toolbar_ctrl=False, draw_ctrl=False)
Map

Map = geemap.Map(data_ctrl=False, toolbar_ctrl=False, draw_ctrl=False) Map

Adding basemaps¶

There are several ways to add basemaps to a map. You can specify the basemap to use in the basemap keyword argument when creating the map. Alternatively, you can add basemap layers to the map using the add_basemap method. Geemap has hundreds of built-in basemaps available that can be easily added to the map with only one line of code.

Built-in basemaps¶

Create a map by specifying the basemap to use as follows. For example, the HYBRID basemap represents the Google Satellite Hybrid basemap.

In [ ]:

Map = geemap.Map(basemap='HYBRID')
Map

Map = geemap.Map(basemap='HYBRID') Map

You can add as many basemaps as you like to the map. For example, the following code adds the OpenTopoMap basemap to the map above:

In [ ]:

Map.add_basemap('OpenTopoMap')

Map.add_basemap('OpenTopoMap')

Print out the first 10 basemaps:

In [ ]:

basemaps = list(geemap.basemaps.keys())
len(geemap.basemaps)

basemaps = list(geemap.basemaps.keys()) len(geemap.basemaps)

In [ ]:

basemaps[:10]

basemaps[:10]

XYZ tiles¶

You can also add XYZ tile layers to the map using the Map.add_tile_layer() method. For example, the following code creates an interactive map and adds the Google Terrain basemap to it:

In [ ]:

Map = geemap.Map()
Map.add_tile_layer(
    url="https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}",
    name="Google Terrain",
    attribution="Google",
)
Map

Map = geemap.Map() Map.add_tile_layer( url="https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}", name="Google Terrain", attribution="Google", ) Map

WMS tiles¶

Similarly, you can add WMS tile layers to the map using the Map.add_wms_layer() method. For example, the following code creates an interactive map and adds the National Land Cover Database (NLCD) 2019 basemap to it:

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
url = 'https://www.mrlc.gov/geoserver/mrlc_display/NLCD_2019_Land_Cover_L48/wms?'
Map.add_wms_layer(
    url=url,
    layers='NLCD_2019_Land_Cover_L48',
    name='NLCD 2019',
    format='image/png',
    attribution='MRLC',
    transparent=True,
)
Map

Map = geemap.Map(center=[40, -100], zoom=4) url = 'https://www.mrlc.gov/geoserver/mrlc_display/NLCD_2019_Land_Cover_L48/wms?' Map.add_wms_layer( url=url, layers='NLCD_2019_Land_Cover_L48', name='NLCD 2019', format='image/png', attribution='MRLC', transparent=True, ) Map

Earth Engine data types¶

Earth Engine objects are server-side objects rather than client-side objects, which means that they are not stored locally on your computer. Similar to video streaming services (e.g., YouTube, Netflix, and Hulu), which store videos/movies on their servers, Earth Engine data are stored on the Earth Engine servers. We can stream geospatial data from Earth Engine on-the-fly without having to download the data just like we can watch videos from streaming services using a web browser without having to download the entire video to your computer.

• Image: the fundamental raster data type in Earth Engine.
• ImageCollection: a stack or time-series of images.
• Geometry: the fundamental vector data type in Earth Engine.
• Feature: a Geometry with attributes.
• FeatureCollection: a set of features.

Image¶

Raster data in Earth Engine are represented as Image objects. Images are composed of one or more bands and each band has its own name, data type, scale, mask and projection. Each image has metadata stored as a set of properties.

Loading Earth Engine images¶

In [ ]:

image = ee.Image('USGS/SRTMGL1_003')
image

image = ee.Image('USGS/SRTMGL1_003') image

Visualizing Earth Engine images¶

In [ ]:

Map = geemap.Map(center=[21.79, 70.87], zoom=3)
image = ee.Image('USGS/SRTMGL1_003')
vis_params = {
    'min': 0,
    'max': 6000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}
Map.addLayer(image, vis_params, 'SRTM')
Map

Map = geemap.Map(center=[21.79, 70.87], zoom=3) image = ee.Image('USGS/SRTMGL1_003') vis_params = { 'min': 0, 'max': 6000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } Map.addLayer(image, vis_params, 'SRTM') Map

ImageCollection¶

An ImageCollection is a stack or sequence of images. An ImageCollection can be loaded by passing an Earth Engine asset ID into the ImageCollection constructor. You can find ImageCollection IDs in the Earth Engine Data Catalog.

Loading image collections¶

For example, to load the image collection of the Sentinel-2 surface reflectance:

In [ ]:

collection = ee.ImageCollection('COPERNICUS/S2_SR')

collection = ee.ImageCollection('COPERNICUS/S2_SR')

Visualizing image collections¶

To visualize an Earth Engine ImageCollection, we need to convert an ImageCollection to an Image by compositing all the images in the collection to a single image representing, for example, the min, max, median, mean or standard deviation of the images. For example, to create a median value image from a collection, use the collection.median() method. Let's create a median image from the Sentinel-2 surface reflectance collection:

In [ ]:

Map = geemap.Map()
collection = ee.ImageCollection('COPERNICUS/S2_SR')
image = collection.median()

vis = {
    'min': 0.0,
    'max': 3000,
    'bands': ['B4', 'B3', 'B2'],
}

Map.setCenter(83.277, 17.7009, 12)
Map.addLayer(image, vis, 'Sentinel-2')
Map

Map = geemap.Map() collection = ee.ImageCollection('COPERNICUS/S2_SR') image = collection.median() vis = { 'min': 0.0, 'max': 3000, 'bands': ['B4', 'B3', 'B2'], } Map.setCenter(83.277, 17.7009, 12) Map.addLayer(image, vis, 'Sentinel-2') Map

Filtering image collections¶

In [ ]:

Map = geemap.Map()
collection = (
    ee.ImageCollection('COPERNICUS/S2_SR')
    .filterDate('2021-01-01', '2022-01-01')
    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 5))
)
image = collection.median()

vis = {
    'min': 0.0,
    'max': 3000,
    'bands': ['B4', 'B3', 'B2'],
}

Map.setCenter(83.277, 17.7009, 12)
Map.addLayer(image, vis, 'Sentinel-2')
Map

Map = geemap.Map() collection = ( ee.ImageCollection('COPERNICUS/S2_SR') .filterDate('2021-01-01', '2022-01-01') .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 5)) ) image = collection.median() vis = { 'min': 0.0, 'max': 3000, 'bands': ['B4', 'B3', 'B2'], } Map.setCenter(83.277, 17.7009, 12) Map.addLayer(image, vis, 'Sentinel-2') Map

FeatureCollection¶

A FeatureCollection is a collection of Features. A FeatureCollection is analogous to a GeoJSON FeatureCollection object, i.e., a collection of features with associated properties/attributes. Data contained in a shapefile can be represented as a FeatureCollection.

Loading feature collections¶

The Earth Engine Data Catalog hosts a variety of vector datasets (e.g,, US Census data, country boundaries, and more) as feature collections. You can find feature collection IDs by searching the data catalog. For example, to load the TIGER roads data by the U.S. Census Bureau:

In [ ]:

Map = geemap.Map()
fc = ee.FeatureCollection('TIGER/2016/Roads')
Map.setCenter(-73.9596, 40.7688, 12)
Map.addLayer(fc, {}, 'Census roads')
Map

Map = geemap.Map() fc = ee.FeatureCollection('TIGER/2016/Roads') Map.setCenter(-73.9596, 40.7688, 12) Map.addLayer(fc, {}, 'Census roads') Map

Filtering feature collections¶

In [ ]:

Map = geemap.Map()
states = ee.FeatureCollection('TIGER/2018/States')
fc = states.filter(ee.Filter.eq('NAME', 'Louisiana'))
Map.addLayer(fc, {}, 'Louisiana')
Map.centerObject(fc)
Map

Map = geemap.Map() states = ee.FeatureCollection('TIGER/2018/States') fc = states.filter(ee.Filter.eq('NAME', 'Louisiana')) Map.addLayer(fc, {}, 'Louisiana') Map.centerObject(fc) Map

In [ ]:

feat = fc.first()
feat.toDictionary()

feat = fc.first() feat.toDictionary()

In [ ]:

Map = geemap.Map()
states = ee.FeatureCollection('TIGER/2018/States')
fc = states.filter(ee.Filter.inList('NAME', ['California', 'Oregon', 'Washington']))
Map.addLayer(fc, {}, 'West Coast')
Map.centerObject(fc)
Map

Map = geemap.Map() states = ee.FeatureCollection('TIGER/2018/States') fc = states.filter(ee.Filter.inList('NAME', ['California', 'Oregon', 'Washington'])) Map.addLayer(fc, {}, 'West Coast') Map.centerObject(fc) Map

In [ ]:

region = Map.user_roi
if region is None:
    region = ee.Geometry.BBox(-88.40, 29.88, -77.90, 35.39)

fc = ee.FeatureCollection('TIGER/2018/States').filterBounds(region)
Map.addLayer(fc, {}, 'Southeastern U.S.')
Map.centerObject(fc)

region = Map.user_roi if region is None: region = ee.Geometry.BBox(-88.40, 29.88, -77.90, 35.39) fc = ee.FeatureCollection('TIGER/2018/States').filterBounds(region) Map.addLayer(fc, {}, 'Southeastern U.S.') Map.centerObject(fc)

Visualizing feature collections¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
Map.addLayer(states, {}, "US States")
Map

Map = geemap.Map(center=[40, -100], zoom=4) states = ee.FeatureCollection("TIGER/2018/States") Map.addLayer(states, {}, "US States") Map

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
style = {'color': '0000ffff', 'width': 2, 'lineType': 'solid', 'fillColor': 'FF000080'}
Map.addLayer(states.style(**style), {}, "US States")
Map

Map = geemap.Map(center=[40, -100], zoom=4) states = ee.FeatureCollection("TIGER/2018/States") style = {'color': '0000ffff', 'width': 2, 'lineType': 'solid', 'fillColor': 'FF000080'} Map.addLayer(states.style(**style), {}, "US States") Map

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
states = ee.FeatureCollection("TIGER/2018/States")
vis_params = {
    'color': '000000',
    'colorOpacity': 1,
    'pointSize': 3,
    'pointShape': 'circle',
    'width': 2,
    'lineType': 'solid',
    'fillColorOpacity': 0.66,
}
palette = ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']
Map.add_styled_vector(
    states, column="NAME", palette=palette, layer_name="Styled vector", **vis_params
)
Map

Map = geemap.Map(center=[40, -100], zoom=4) states = ee.FeatureCollection("TIGER/2018/States") vis_params = { 'color': '000000', 'colorOpacity': 1, 'pointSize': 3, 'pointShape': 'circle', 'width': 2, 'lineType': 'solid', 'fillColorOpacity': 0.66, } palette = ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'] Map.add_styled_vector( states, column="NAME", palette=palette, layer_name="Styled vector", **vis_params ) Map

Earth Engine Data Catalog¶

The Earth Engine Data Catalog hosts a variety of geospatial datasets. As of March 2023, the catalog contains over 1,000 datasets with a total size of over 40 petabytes. Some notable datasets include: Landsat, Sentinel, MODIS, NAIP, etc. For a complete list of datasets in CSV or JSON formats, see the Earth Engine Datasets List.

Searching for datasets¶

The Earth Engine Data Catalog is searchable. You can search datasets by name, keyword, or tag. For example, enter "elevation" in the search box will filter the catalog to show only datasets containing "elevation" in their name, description, or tags. 52 datasets are returned for this search query. Scroll down the list to find the NASA SRTM Digital Elevation 30m dataset. On each dataset page, you can find the following information, including Dataset Availability, Dataset Provider, Earth Engine Snippet, Tags, Description, Code Example, and more (see {numref}ch03_gee_srtm). One important piece of information is the Image/ImageCollection/FeatureCollection ID of each dataset, which is essential for accessing the dataset through the Earth Engine JavaScript or Python APIs.

In [ ]:

dataset_xyz = ee.Image('USGS/SRTMGL1_003')
Map.addLayer(dataset_xyz, {}, "USGS/SRTMGL1_003")

dataset_xyz = ee.Image('USGS/SRTMGL1_003') Map.addLayer(dataset_xyz, {}, "USGS/SRTMGL1_003")

In [ ]:

Map = geemap.Map()
dem = ee.Image('USGS/SRTMGL1_003')
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}
Map.addLayer(dem, vis_params, 'SRTM DEM')
Map

Map = geemap.Map() dem = ee.Image('USGS/SRTMGL1_003') vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } Map.addLayer(dem, vis_params, 'SRTM DEM') Map

Using the datasets module¶

In [ ]:

from geemap.datasets import DATA

from geemap.datasets import DATA

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
dataset = ee.Image(DATA.USGS_GAP_CONUS_2011)
Map.addLayer(dataset, {}, 'GAP CONUS')
Map

Map = geemap.Map(center=[40, -100], zoom=4) dataset = ee.Image(DATA.USGS_GAP_CONUS_2011) Map.addLayer(dataset, {}, 'GAP CONUS') Map

In [ ]:

from geemap.datasets import get_metadata

get_metadata(DATA.USGS_GAP_CONUS_2011)

from geemap.datasets import get_metadata get_metadata(DATA.USGS_GAP_CONUS_2011)

Using the inspector tool¶

In [ ]:

Map = geemap.Map(center=(40, -100), zoom=4)

dem = ee.Image('USGS/SRTMGL1_003')
landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(
    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']
)
states = ee.FeatureCollection("TIGER/2018/States")

vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

Map.addLayer(dem, vis_params, 'SRTM DEM')
Map.addLayer(
    landsat7,
    {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 2.0},
    'Landsat 7',
)
Map.addLayer(states, {}, "US States")
Map

Map = geemap.Map(center=(40, -100), zoom=4) dem = ee.Image('USGS/SRTMGL1_003') landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select( ['B1', 'B2', 'B3', 'B4', 'B5', 'B7'] ) states = ee.FeatureCollection("TIGER/2018/States") vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } Map.addLayer(dem, vis_params, 'SRTM DEM') Map.addLayer( landsat7, {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 2.0}, 'Landsat 7', ) Map.addLayer(states, {}, "US States") Map

Converting JavaScript to Python¶

Find some Earth Engine JavaScript code that you want to convert to Python. For example, you can grab some sample code from the Earth Engine Documentation.

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

# Load an image.
image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318')

# Define the visualization parameters.
vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]}

# Center the map and display the image.
Map.setCenter(-122.1899, 37.5010, 10)
# San Francisco Bay
Map.addLayer(image, vizParams, 'False color composite')

# Load an image. image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318') # Define the visualization parameters. vizParams = {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]} # Center the map and display the image. Map.setCenter(-122.1899, 37.5010, 10) # San Francisco Bay Map.addLayer(image, vizParams, 'False color composite')

Using the plotting tool¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)

landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(
    ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']
)

landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(landsat7, landsat_vis, "Landsat")

hyperion = ee.ImageCollection('EO1/HYPERION').filter(
    ee.Filter.date('2016-01-01', '2017-03-01')
)

hyperion_vis = {
    'min': 1000.0,
    'max': 14000.0,
    'gamma': 2.5,
}
Map.addLayer(hyperion, hyperion_vis, 'Hyperion')
Map

Map = geemap.Map(center=[40, -100], zoom=4) landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select( ['B1', 'B2', 'B3', 'B4', 'B5', 'B7'] ) landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4} Map.addLayer(landsat7, landsat_vis, "Landsat") hyperion = ee.ImageCollection('EO1/HYPERION').filter( ee.Filter.date('2016-01-01', '2017-03-01') ) hyperion_vis = { 'min': 1000.0, 'max': 14000.0, 'gamma': 2.5, } Map.addLayer(hyperion, hyperion_vis, 'Hyperion') Map

In [ ]:

Map.set_plot_options(add_marker_cluster=True, overlay=True)

Map.set_plot_options(add_marker_cluster=True, overlay=True)

Creating legends¶

Built-in legends¶

In [ ]:

legends = geemap.builtin_legends
for legend in legends:
    print(legend)

legends = geemap.builtin_legends for legend in legends: print(legend)

In [ ]:

Map.add_legend(builtin_legend='NLCD')

Map.add_legend(builtin_legend='NLCD')

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
Map.add_basemap('HYBRID')

nlcd = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019')
landcover = nlcd.select('landcover')

Map.addLayer(landcover, {}, 'NLCD Land Cover 2019')
Map.add_legend(
    title="NLCD Land Cover Classification", builtin_legend='NLCD'
)
Map

Map = geemap.Map(center=[40, -100], zoom=4) Map.add_basemap('HYBRID') nlcd = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019') landcover = nlcd.select('landcover') Map.addLayer(landcover, {}, 'NLCD Land Cover 2019') Map.add_legend( title="NLCD Land Cover Classification", builtin_legend='NLCD' ) Map

Custom legends¶

In [ ]:

Map = geemap.Map(add_google_map=False)

labels = ['One', 'Two', 'Three', 'Four', 'ect']

# colors can be defined using either hex code or RGB (0-255, 0-255, 0-255)
colors = ['#8DD3C7', '#FFFFB3', '#BEBADA', '#FB8072', '#80B1D3']
# legend_colors = [(255, 0, 0), (127, 255, 0), (127, 18, 25), (36, 70, 180), (96, 68 123)]

Map.add_legend(
    labels=labels, colors=colors, position='bottomright'
)
Map

Map = geemap.Map(add_google_map=False) labels = ['One', 'Two', 'Three', 'Four', 'ect'] # colors can be defined using either hex code or RGB (0-255, 0-255, 0-255) colors = ['#8DD3C7', '#FFFFB3', '#BEBADA', '#FB8072', '#80B1D3'] # legend_colors = [(255, 0, 0), (127, 255, 0), (127, 18, 25), (36, 70, 180), (96, 68 123)] Map.add_legend( labels=labels, colors=colors, position='bottomright' ) Map

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)

legend_dict = {
    '11 Open Water': '466b9f',
    '12 Perennial Ice/Snow': 'd1def8',
    '21 Developed, Open Space': 'dec5c5',
    '22 Developed, Low Intensity': 'd99282',
    '23 Developed, Medium Intensity': 'eb0000',
    '24 Developed High Intensity': 'ab0000',
    '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f',
    '41 Deciduous Forest': '68ab5f',
    '42 Evergreen Forest': '1c5f2c',
    '43 Mixed Forest': 'b5c58f',
    '51 Dwarf Scrub': 'af963c',
    '52 Shrub/Scrub': 'ccb879',
    '71 Grassland/Herbaceous': 'dfdfc2',
    '72 Sedge/Herbaceous': 'd1d182',
    '73 Lichens': 'a3cc51',
    '74 Moss': '82ba9e',
    '81 Pasture/Hay': 'dcd939',
    '82 Cultivated Crops': 'ab6c28',
    '90 Woody Wetlands': 'b8d9eb',
    '95 Emergent Herbaceous Wetlands': '6c9fb8',
}

nlcd = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019')
landcover = nlcd.select('landcover')

Map.addLayer(landcover, {}, 'NLCD Land Cover 2019')
Map.add_legend(title="NLCD Land Cover Classification", legend_dict=legend_dict)
Map

Map = geemap.Map(center=[40, -100], zoom=4) legend_dict = { '11 Open Water': '466b9f', '12 Perennial Ice/Snow': 'd1def8', '21 Developed, Open Space': 'dec5c5', '22 Developed, Low Intensity': 'd99282', '23 Developed, Medium Intensity': 'eb0000', '24 Developed High Intensity': 'ab0000', '31 Barren Land (Rock/Sand/Clay)': 'b3ac9f', '41 Deciduous Forest': '68ab5f', '42 Evergreen Forest': '1c5f2c', '43 Mixed Forest': 'b5c58f', '51 Dwarf Scrub': 'af963c', '52 Shrub/Scrub': 'ccb879', '71 Grassland/Herbaceous': 'dfdfc2', '72 Sedge/Herbaceous': 'd1d182', '73 Lichens': 'a3cc51', '74 Moss': '82ba9e', '81 Pasture/Hay': 'dcd939', '82 Cultivated Crops': 'ab6c28', '90 Woody Wetlands': 'b8d9eb', '95 Emergent Herbaceous Wetlands': '6c9fb8', } nlcd = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019') landcover = nlcd.select('landcover') Map.addLayer(landcover, {}, 'NLCD Land Cover 2019') Map.add_legend(title="NLCD Land Cover Classification", legend_dict=legend_dict) Map

Creating color bars¶

In [ ]:

Map = geemap.Map()

dem = ee.Image('USGS/SRTMGL1_003')
vis_params = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}

Map.addLayer(dem, vis_params, 'SRTM DEM')

Map = geemap.Map() dem = ee.Image('USGS/SRTMGL1_003') vis_params = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } Map.addLayer(dem, vis_params, 'SRTM DEM')

In [ ]:

Map.add_colorbar(vis_params, label="Elevation (m)", layer_name="SRTM DEM")

Map.add_colorbar(vis_params, label="Elevation (m)", layer_name="SRTM DEM")

In [ ]:

Map.add_colorbar(
    vis_params, label="Elevation (m)", layer_name="SRTM DEM", orientation="vertical"
)

Map.add_colorbar( vis_params, label="Elevation (m)", layer_name="SRTM DEM", orientation="vertical" )

In [ ]:

Map.add_colorbar(
    vis_params,
    label="Elevation (m)",
    layer_name="SRTM DEM",
    orientation="vertical",
    transparent_bg=True,
)
Map

Map.add_colorbar( vis_params, label="Elevation (m)", layer_name="SRTM DEM", orientation="vertical", transparent_bg=True, ) Map

Split-panel maps¶

In [ ]:

Map = geemap.Map()
Map.split_map(left_layer='HYBRID', right_layer='TERRAIN')
Map

Map = geemap.Map() Map.split_map(left_layer='HYBRID', right_layer='TERRAIN') Map

In [ ]:

Map = geemap.Map(center=(40, -100), zoom=4, height=600)

nlcd_2001 = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2001').select('landcover')
nlcd_2019 = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019').select('landcover')

left_layer = geemap.ee_tile_layer(nlcd_2001, {}, 'NLCD 2001')
right_layer = geemap.ee_tile_layer(nlcd_2019, {}, 'NLCD 2019')

Map.split_map(left_layer, right_layer)
Map

Map = geemap.Map(center=(40, -100), zoom=4, height=600) nlcd_2001 = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2001').select('landcover') nlcd_2019 = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019').select('landcover') left_layer = geemap.ee_tile_layer(nlcd_2001, {}, 'NLCD 2001') right_layer = geemap.ee_tile_layer(nlcd_2019, {}, 'NLCD 2019') Map.split_map(left_layer, right_layer) Map

Linked maps¶

In [ ]:

image = (
    ee.ImageCollection('COPERNICUS/S2')
    .filterDate('2018-09-01', '2018-09-30')
    .map(lambda img: img.divide(10000))
    .median()
)

vis_params = [
    {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
    {'bands': ['B8', 'B11', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
    {'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
    {'bands': ['B12', 'B12', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3},
]

labels = [
    'Natural Color (B4/B3/B2)',
    'Land/Water (B8/B11/B4)',
    'Color Infrared (B8/B4/B3)',
    'Vegetation (B12/B11/B4)',
]

geemap.linked_maps(
    rows=2,
    cols=2,
    height="300px",
    center=[38.4151, 21.2712],
    zoom=12,
    ee_objects=[image],
    vis_params=vis_params,
    labels=labels,
    label_position="topright",
)

image = ( ee.ImageCollection('COPERNICUS/S2') .filterDate('2018-09-01', '2018-09-30') .map(lambda img: img.divide(10000)) .median() ) vis_params = [ {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 0.3, 'gamma': 1.3}, {'bands': ['B8', 'B11', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3}, {'bands': ['B8', 'B4', 'B3'], 'min': 0, 'max': 0.3, 'gamma': 1.3}, {'bands': ['B12', 'B12', 'B4'], 'min': 0, 'max': 0.3, 'gamma': 1.3}, ] labels = [ 'Natural Color (B4/B3/B2)', 'Land/Water (B8/B11/B4)', 'Color Infrared (B8/B4/B3)', 'Vegetation (B12/B11/B4)', ] geemap.linked_maps( rows=2, cols=2, height="300px", center=[38.4151, 21.2712], zoom=12, ee_objects=[image], vis_params=vis_params, labels=labels, label_position="topright", )

Timeseries inspector¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
collection = ee.ImageCollection('USGS/NLCD_RELEASES/2019_REL/NLCD').select('landcover')
vis_params = {'bands': ['landcover']}
years = collection.aggregate_array('system:index').getInfo()
years

Map = geemap.Map(center=[40, -100], zoom=4) collection = ee.ImageCollection('USGS/NLCD_RELEASES/2019_REL/NLCD').select('landcover') vis_params = {'bands': ['landcover']} years = collection.aggregate_array('system:index').getInfo() years

In [ ]:

Map.ts_inspector(
    left_ts=collection,
    right_ts=collection,
    left_names=years,
    right_names=years,
    left_vis=vis_params,
    right_vis=vis_params,
    width='80px',
)
Map

Map.ts_inspector( left_ts=collection, right_ts=collection, left_names=years, right_names=years, left_vis=vis_params, right_vis=vis_params, width='80px', ) Map

Time slider¶

Visualizing vegetation data¶

In [ ]:

Map = geemap.Map()

collection = (
    ee.ImageCollection('MODIS/MCD43A4_006_NDVI')
    .filter(ee.Filter.date('2018-06-01', '2018-07-01'))
    .select("NDVI")
)
vis_params = {
    'min': 0.0,
    'max': 1.0,
    'palette': 'ndvi',
}

Map.add_time_slider(collection, vis_params, time_interval=2)
Map

Map = geemap.Map() collection = ( ee.ImageCollection('MODIS/MCD43A4_006_NDVI') .filter(ee.Filter.date('2018-06-01', '2018-07-01')) .select("NDVI") ) vis_params = { 'min': 0.0, 'max': 1.0, 'palette': 'ndvi', } Map.add_time_slider(collection, vis_params, time_interval=2) Map

Visualizing weather data¶

In [ ]:

Map = geemap.Map()

collection = (
    ee.ImageCollection('NOAA/GFS0P25')
    .filterDate('2018-12-22', '2018-12-23')
    .limit(24)
    .select('temperature_2m_above_ground')
)

vis_params = {
    'min': -40.0,
    'max': 35.0,
    'palette': ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'],
}

labels = [str(n).zfill(2) + ":00" for n in range(0, 24)]
Map.add_time_slider(collection, vis_params, labels=labels, time_interval=1, opacity=0.8)
Map

Map = geemap.Map() collection = ( ee.ImageCollection('NOAA/GFS0P25') .filterDate('2018-12-22', '2018-12-23') .limit(24) .select('temperature_2m_above_ground') ) vis_params = { 'min': -40.0, 'max': 35.0, 'palette': ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'], } labels = [str(n).zfill(2) + ":00" for n in range(0, 24)] Map.add_time_slider(collection, vis_params, labels=labels, time_interval=1, opacity=0.8) Map

Visualizing Sentinel-2 imagery¶

In [ ]:

Map = geemap.Map(center=[37.75, -122.45], zoom=12)

collection = (
    ee.ImageCollection('COPERNICUS/S2_SR')
    .filterBounds(ee.Geometry.Point([-122.45, 37.75]))
    .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10)
)

vis_params = {"min": 0, "max": 4000, "bands": ["B8", "B4", "B3"]}

Map.add_time_slider(collection, vis_params)
Map

Map = geemap.Map(center=[37.75, -122.45], zoom=12) collection = ( ee.ImageCollection('COPERNICUS/S2_SR') .filterBounds(ee.Geometry.Point([-122.45, 37.75])) .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10) ) vis_params = {"min": 0, "max": 4000, "bands": ["B8", "B4", "B3"]} Map.add_time_slider(collection, vis_params) Map

Zonal statistics with Earth Engine¶

Zonal statistics¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)

# Add NASA SRTM
dem = ee.Image('USGS/SRTMGL1_003')
dem_vis = {
    'min': 0,
    'max': 4000,
    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
}
Map.addLayer(dem, dem_vis, 'SRTM DEM')

# Add 5-year Landsat TOA composite
landsat = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')
landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4}
Map.addLayer(landsat, landsat_vis, "Landsat", False)

# Add US Census States
states = ee.FeatureCollection("TIGER/2018/States")
style = {'fillColor': '00000000'}
Map.addLayer(states.style(**style), {}, 'US States')
Map

Map = geemap.Map(center=[40, -100], zoom=4) # Add NASA SRTM dem = ee.Image('USGS/SRTMGL1_003') dem_vis = { 'min': 0, 'max': 4000, 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'], } Map.addLayer(dem, dem_vis, 'SRTM DEM') # Add 5-year Landsat TOA composite landsat = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003') landsat_vis = {'bands': ['B4', 'B3', 'B2'], 'gamma': 1.4} Map.addLayer(landsat, landsat_vis, "Landsat", False) # Add US Census States states = ee.FeatureCollection("TIGER/2018/States") style = {'fillColor': '00000000'} Map.addLayer(states.style(**style), {}, 'US States') Map

In [ ]:

out_dem_stats = 'dem_stats.csv'
geemap.zonal_stats(
    dem, states, out_dem_stats, statistics_type='MEAN', scale=1000, return_fc=False
)

out_dem_stats = 'dem_stats.csv' geemap.zonal_stats( dem, states, out_dem_stats, statistics_type='MEAN', scale=1000, return_fc=False )

In [ ]:

out_landsat_stats = 'landsat_stats.csv'
geemap.zonal_stats(
    landsat,
    states,
    out_landsat_stats,
    statistics_type='MEAN',
    scale=1000,
    return_fc=False,
)

out_landsat_stats = 'landsat_stats.csv' geemap.zonal_stats( landsat, states, out_landsat_stats, statistics_type='MEAN', scale=1000, return_fc=False, )

Zonal statistics by group¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)

# Add NLCD data
dataset = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019')
landcover = dataset.select('landcover')
Map.addLayer(landcover, {}, 'NLCD 2019')

# Add US census states
states = ee.FeatureCollection("TIGER/2018/States")
style = {'fillColor': '00000000'}
Map.addLayer(states.style(**style), {}, 'US States')

# Add NLCD legend
Map.add_legend(title='NLCD Land Cover', builtin_legend='NLCD')
Map

Map = geemap.Map(center=[40, -100], zoom=4) # Add NLCD data dataset = ee.Image('USGS/NLCD_RELEASES/2019_REL/NLCD/2019') landcover = dataset.select('landcover') Map.addLayer(landcover, {}, 'NLCD 2019') # Add US census states states = ee.FeatureCollection("TIGER/2018/States") style = {'fillColor': '00000000'} Map.addLayer(states.style(**style), {}, 'US States') # Add NLCD legend Map.add_legend(title='NLCD Land Cover', builtin_legend='NLCD') Map

In [ ]:

nlcd_stats = 'nlcd_stats.csv'

geemap.zonal_stats_by_group(
    landcover,
    states,
    nlcd_stats,
    statistics_type='SUM',
    denominator=1e6,
    decimal_places=2,
)

nlcd_stats = 'nlcd_stats.csv' geemap.zonal_stats_by_group( landcover, states, nlcd_stats, statistics_type='SUM', denominator=1e6, decimal_places=2, )

In [ ]:

nlcd_stats = 'nlcd_stats_pct.csv'

geemap.zonal_stats_by_group(
    landcover,
    states,
    nlcd_stats,
    statistics_type='PERCENTAGE',
    denominator=1e6,
    decimal_places=2,
)

nlcd_stats = 'nlcd_stats_pct.csv' geemap.zonal_stats_by_group( landcover, states, nlcd_stats, statistics_type='PERCENTAGE', denominator=1e6, decimal_places=2, )

Zonal statistics with two images¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)
dem = ee.Image('USGS/3DEP/10m')
vis = {'min': 0, 'max': 4000, 'palette': 'terrain'}
Map.addLayer(dem, vis, 'DEM')
Map

Map = geemap.Map(center=[40, -100], zoom=4) dem = ee.Image('USGS/3DEP/10m') vis = {'min': 0, 'max': 4000, 'palette': 'terrain'} Map.addLayer(dem, vis, 'DEM') Map

In [ ]:

landcover = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select('landcover')
Map.addLayer(landcover, {}, 'NLCD 2019')
Map.add_legend(title='NLCD Land Cover Classification', builtin_legend='NLCD')

landcover = ee.Image("USGS/NLCD_RELEASES/2019_REL/NLCD/2019").select('landcover') Map.addLayer(landcover, {}, 'NLCD 2019') Map.add_legend(title='NLCD Land Cover Classification', builtin_legend='NLCD')

In [ ]:

stats = geemap.image_stats_by_zone(dem, landcover, reducer='MEAN')
stats

stats = geemap.image_stats_by_zone(dem, landcover, reducer='MEAN') stats

In [ ]:

stats.to_csv('mean.csv', index=False)

stats.to_csv('mean.csv', index=False)

In [ ]:

geemap.image_stats_by_zone(dem, landcover, out_csv="std.csv", reducer='STD')

geemap.image_stats_by_zone(dem, landcover, out_csv="std.csv", reducer='STD')

Exporting images¶

In [ ]:

Map = geemap.Map()

image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318').select(
    ['B5', 'B4', 'B3']
)

vis_params = {'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]}

Map.centerObject(image)
Map.addLayer(image, vis_params, 'Landsat')
Map

Map = geemap.Map() image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318').select( ['B5', 'B4', 'B3'] ) vis_params = {'min': 0, 'max': 0.5, 'gamma': [0.95, 1.1, 1]} Map.centerObject(image) Map.addLayer(image, vis_params, 'Landsat') Map

In [ ]:

region = ee.Geometry.BBox(-122.5955, 37.5339, -122.0982, 37.8252)
fc = ee.FeatureCollection(region)
style = {'color': 'ffff00ff', 'fillColor': '00000000'}
Map.addLayer(fc.style(**style), {}, 'ROI')
Map

region = ee.Geometry.BBox(-122.5955, 37.5339, -122.0982, 37.8252) fc = ee.FeatureCollection(region) style = {'color': 'ffff00ff', 'fillColor': '00000000'} Map.addLayer(fc.style(**style), {}, 'ROI') Map

To local drive¶

In [ ]:

geemap.ee_export_image(image, filename="landsat.tif", scale=30, region=region)

geemap.ee_export_image(image, filename="landsat.tif", scale=30, region=region)

To Google Drive¶

In [ ]:

geemap.ee_export_image_to_drive(
    image, description='landsat', folder='export', region=region, scale=30
)

geemap.ee_export_image_to_drive( image, description='landsat', folder='export', region=region, scale=30 )

To Asset¶

In [ ]:

assetId = 'landsat_sfo'
geemap.ee_export_image_to_asset(
    image, description='landsat', assetId=assetId, region=region, scale=30
)

assetId = 'landsat_sfo' geemap.ee_export_image_to_asset( image, description='landsat', assetId=assetId, region=region, scale=30 )

Exporting image collections¶

In [ ]:

point = ee.Geometry.Point(-99.2222, 46.7816)
collection = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(point)
    .filterDate('2008-01-01', '2018-01-01')
    .filter(ee.Filter.listContains("system:band_names", "N"))
)

point = ee.Geometry.Point(-99.2222, 46.7816) collection = ( ee.ImageCollection('USDA/NAIP/DOQQ') .filterBounds(point) .filterDate('2008-01-01', '2018-01-01') .filter(ee.Filter.listContains("system:band_names", "N")) )

In [ ]:

collection.aggregate_array('system:index')

collection.aggregate_array('system:index')

To local drive¶

In [ ]:

geemap.ee_export_image_collection(collection, out_dir='.', scale=10)

geemap.ee_export_image_collection(collection, out_dir='.', scale=10)

To Google Drive¶

In [ ]:

geemap.ee_export_image_collection_to_drive(collection, folder='export', scale=10)

geemap.ee_export_image_collection_to_drive(collection, folder='export', scale=10)

Exporting feature collections¶

In [ ]:

Map = geemap.Map()
states = ee.FeatureCollection('TIGER/2018/States')
fc = states.filter(ee.Filter.eq('NAME', 'Louisiana'))
Map.addLayer(fc, {}, 'Louisiana')
Map.centerObject(fc)
Map

Map = geemap.Map() states = ee.FeatureCollection('TIGER/2018/States') fc = states.filter(ee.Filter.eq('NAME', 'Louisiana')) Map.addLayer(fc, {}, 'Louisiana') Map.centerObject(fc) Map

To local drive¶

In [ ]:

geemap.ee_to_shp(fc, filename='louisiana.shp', selectors=None)

geemap.ee_to_shp(fc, filename='louisiana.shp', selectors=None)

In [ ]:

geemap.ee_to_geojson(fc, filename='louisiana.geojson')

geemap.ee_to_geojson(fc, filename='louisiana.geojson')

In [ ]:

geemap.ee_to_csv(fc, filename='louisiana.csv')

geemap.ee_to_csv(fc, filename='louisiana.csv')

In [ ]:

df = geemap.ee_to_df(fc)
df

df = geemap.ee_to_df(fc) df

To Google Drive¶

In [ ]:

geemap.ee_export_vector_to_drive(
    fc, description="louisiana", fileFormat='SHP', folder="export"
)

geemap.ee_export_vector_to_drive( fc, description="louisiana", fileFormat='SHP', folder="export" )

Creating timeseries¶

In [ ]:

collection = ee.ImageCollection("COPERNICUS/S2_HARMONIZED").filterMetadata(
    'CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10
)

collection = ee.ImageCollection("COPERNICUS/S2_HARMONIZED").filterMetadata( 'CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10 )

In [ ]:

start_date = '2016-01-01'
end_date = '2022-12-31'
region = ee.Geometry.BBox(-122.5549, 37.6968, -122.3446, 37.8111)

start_date = '2016-01-01' end_date = '2022-12-31' region = ee.Geometry.BBox(-122.5549, 37.6968, -122.3446, 37.8111)

In [ ]:

images = geemap.create_timeseries(
    collection, start_date, end_date, region, frequency='year', reducer='median'
)
images

images = geemap.create_timeseries( collection, start_date, end_date, region, frequency='year', reducer='median' ) images

In [ ]:

Map = geemap.Map()

vis_params = {"min": 0, "max": 4000, "bands": ["B8", "B4", "B3"]}
labels = [str(y) for y in range(2016, 2023)]

Map.addLayer(images, vis_params, "Sentinel-2", False)
Map.add_time_slider(images, vis_params, time_interval=2, labels=labels)
Map.centerObject(region)
Map

Map = geemap.Map() vis_params = {"min": 0, "max": 4000, "bands": ["B8", "B4", "B3"]} labels = [str(y) for y in range(2016, 2023)] Map.addLayer(images, vis_params, "Sentinel-2", False) Map.add_time_slider(images, vis_params, time_interval=2, labels=labels) Map.centerObject(region) Map

Creating timelapse¶

Landsat timelapse¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

roi = Map.user_roi
if roi is None:
    roi = ee.Geometry.BBox(-74.7222, -8.5867, -74.1596, -8.2824)
    Map.addLayer(roi)
    Map.centerObject(roi)

roi = Map.user_roi if roi is None: roi = ee.Geometry.BBox(-74.7222, -8.5867, -74.1596, -8.2824) Map.addLayer(roi) Map.centerObject(roi)

In [ ]:

timelapse = geemap.landsat_timelapse(
    roi,
    out_gif='landsat.gif',
    start_year=1984,
    end_year=2022,
    start_date='01-01',
    end_date='12-31',
    bands=['SWIR1', 'NIR', 'Red'],
    frames_per_second=5,
    title='Landsat Timelapse',
    progress_bar_color='blue',
    mp4=True,
)
geemap.show_image(timelapse)

timelapse = geemap.landsat_timelapse( roi, out_gif='landsat.gif', start_year=1984, end_year=2022, start_date='01-01', end_date='12-31', bands=['SWIR1', 'NIR', 'Red'], frames_per_second=5, title='Landsat Timelapse', progress_bar_color='blue', mp4=True, ) geemap.show_image(timelapse)

In [ ]:

Map = geemap.Map()
roi = ee.Geometry.BBox(-115.5541, 35.8044, -113.9035, 36.5581)
Map.addLayer(roi)
Map.centerObject(roi)
Map

Map = geemap.Map() roi = ee.Geometry.BBox(-115.5541, 35.8044, -113.9035, 36.5581) Map.addLayer(roi) Map.centerObject(roi) Map

In [ ]:

timelapse = geemap.landsat_timelapse(
    roi,
    out_gif='las_vegas.gif',
    start_year=1984,
    end_year=2022,
    bands=['NIR', 'Red', 'Green'],
    frames_per_second=5,
    title='Las Vegas, NV',
    font_color='blue',
)
geemap.show_image(timelapse)

timelapse = geemap.landsat_timelapse( roi, out_gif='las_vegas.gif', start_year=1984, end_year=2022, bands=['NIR', 'Red', 'Green'], frames_per_second=5, title='Las Vegas, NV', font_color='blue', ) geemap.show_image(timelapse)

In [ ]:

Map = geemap.Map()
roi = ee.Geometry.BBox(113.8252, 22.1988, 114.0851, 22.3497)
Map.addLayer(roi)
Map.centerObject(roi)
Map

Map = geemap.Map() roi = ee.Geometry.BBox(113.8252, 22.1988, 114.0851, 22.3497) Map.addLayer(roi) Map.centerObject(roi) Map

In [ ]:

timelapse = geemap.landsat_timelapse(
    roi,
    out_gif='hong_kong.gif',
    start_year=1990,
    end_year=2022,
    start_date='01-01',
    end_date='12-31',
    bands=['SWIR1', 'NIR', 'Red'],
    frames_per_second=3,
    title='Hong Kong',
)
geemap.show_image(timelapse)

timelapse = geemap.landsat_timelapse( roi, out_gif='hong_kong.gif', start_year=1990, end_year=2022, start_date='01-01', end_date='12-31', bands=['SWIR1', 'NIR', 'Red'], frames_per_second=3, title='Hong Kong', ) geemap.show_image(timelapse)

MODIS timelapse¶

In [ ]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

In [ ]:

roi = Map.user_roi
if roi is None:
    roi = ee.Geometry.BBox(-18.6983, -36.1630, 52.2293, 38.1446)
    Map.addLayer(roi)
    Map.centerObject(roi)

roi = Map.user_roi if roi is None: roi = ee.Geometry.BBox(-18.6983, -36.1630, 52.2293, 38.1446) Map.addLayer(roi) Map.centerObject(roi)

In [ ]:

timelapse = geemap.modis_ndvi_timelapse(
    roi,
    out_gif='ndvi.gif',
    data='Terra',
    band='NDVI',
    start_date='2000-01-01',
    end_date='2022-12-31',
    frames_per_second=3,
    title='MODIS NDVI Timelapse',
    overlay_data='countries',
)
geemap.show_image(timelapse)

timelapse = geemap.modis_ndvi_timelapse( roi, out_gif='ndvi.gif', data='Terra', band='NDVI', start_date='2000-01-01', end_date='2022-12-31', frames_per_second=3, title='MODIS NDVI Timelapse', overlay_data='countries', ) geemap.show_image(timelapse)

Analyzing surface water dynamics¶

Surface water occurrence¶

In [77]:

dataset = ee.Image('JRC/GSW1_4/GlobalSurfaceWater')
dataset.bandNames()

dataset = ee.Image('JRC/GSW1_4/GlobalSurfaceWater') dataset.bandNames()

Out[77]:

• List (7 elements)
  • 0:occurrence
  • 1:change_abs
  • 2:change_norm
  • 3:seasonality
  • 4:recurrence
  • 5:transition
  • 6:max_extent

In [78]:

Map = geemap.Map()
Map.add_basemap('HYBRID')
Map

Map = geemap.Map() Map.add_basemap('HYBRID') Map

Out[78]:

In [79]:

image = dataset.select(['occurrence'])
region = Map.user_roi # Draw a polygon on the map
if region is None:
    region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531)
vis_params = {'min': 0.0, 'max': 100.0, 'palette': ['ffffff', 'ffbbbb', '0000ff']}
Map.addLayer(image, vis_params, 'Occurrence')
Map.addLayer(region, {}, 'ROI', True, 0.5)
Map.centerObject(region)
Map.add_colorbar(vis_params, label='Water occurrence (%)', layer_name='Occurrence')

image = dataset.select(['occurrence']) region = Map.user_roi # Draw a polygon on the map if region is None: region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531) vis_params = {'min': 0.0, 'max': 100.0, 'palette': ['ffffff', 'ffbbbb', '0000ff']} Map.addLayer(image, vis_params, 'Occurrence') Map.addLayer(region, {}, 'ROI', True, 0.5) Map.centerObject(region) Map.add_colorbar(vis_params, label='Water occurrence (%)', layer_name='Occurrence')

In [80]:

df = geemap.image_histogram(
    image,
    region,
    scale=30,
    return_df=True,
)
df

df = geemap.image_histogram( image, region, scale=30, return_df=True, ) df

Out[80]:

	key	value
0	0	7.007843
1	1	67.749020
2	2	181.384314
3	3	223.658824
4	4	262.384314
...	...	...
95	95	22259.713725
96	96	28187.235294
97	97	15168.403922
98	98	1817.615686
99	99	13.886275

100 rows × 2 columns

In [81]:

hist = geemap.image_histogram(
    image,
    region,
    scale=30,
    x_label='Water Occurrence (%)',
    y_label='Pixel Count',
    title='Surface Water Occurrence',
    layout_args={'title': dict(x=0.5)},
    return_df=False,
)
hist

hist = geemap.image_histogram( image, region, scale=30, x_label='Water Occurrence (%)', y_label='Pixel Count', title='Surface Water Occurrence', layout_args={'title': dict(x=0.5)}, return_df=False, ) hist

In [82]:

hist.update_layout(
    autosize=False,
    width=800,
    height=400,
    margin=dict(l=30, r=20, b=10, t=50, pad=4)
)

hist.update_layout( autosize=False, width=800, height=400, margin=dict(l=30, r=20, b=10, t=50, pad=4) )

Surface water monthly history¶

In [83]:

dataset = ee.ImageCollection('JRC/GSW1_4/MonthlyHistory')
dataset.size()

dataset = ee.ImageCollection('JRC/GSW1_4/MonthlyHistory') dataset.size()

Out[83]:

• 454

In [84]:

dataset.aggregate_array("system:index")

dataset.aggregate_array("system:index")

Out[84]:

• List (454 elements)
  • 0:1984_03
  • 1:1984_04
  • 2:1984_05
  • 3:1984_06
  • 4:1984_07
  • 5:1984_08
  • 6:1984_09
  • 7:1984_10
  • 8:1984_11
  • 9:1984_12
  • 10:1985_01
  • 11:1985_02
  • 12:1985_03
  • 13:1985_04
  • 14:1985_05
  • 15:1985_06
  • 16:1985_07
  • 17:1985_08
  • 18:1985_09
  • 19:1985_10
  • 20:1985_11
  • 21:1985_12
  • 22:1986_01
  • 23:1986_02
  • 24:1986_03
  • 25:1986_04
  • 26:1986_05
  • 27:1986_06
  • 28:1986_07
  • 29:1986_08
  • 30:1986_09
  • 31:1986_10
  • 32:1986_11
  • 33:1986_12
  • 34:1987_01
  • 35:1987_02
  • 36:1987_03
  • 37:1987_04
  • 38:1987_05
  • 39:1987_06
  • 40:1987_07
  • 41:1987_08
  • 42:1987_09
  • 43:1987_10
  • 44:1987_11
  • 45:1987_12
  • 46:1988_01
  • 47:1988_02
  • 48:1988_03
  • 49:1988_04
  • 50:1988_05
  • 51:1988_06
  • 52:1988_07
  • 53:1988_08
  • 54:1988_09
  • 55:1988_10
  • 56:1988_11
  • 57:1988_12
  • 58:1989_01
  • 59:1989_02
  • 60:1989_03
  • 61:1989_04
  • 62:1989_05
  • 63:1989_06
  • 64:1989_07
  • 65:1989_08
  • 66:1989_09
  • 67:1989_10
  • 68:1989_11
  • 69:1989_12
  • 70:1990_01
  • 71:1990_02
  • 72:1990_03
  • 73:1990_04
  • 74:1990_05
  • 75:1990_06
  • 76:1990_07
  • 77:1990_08
  • 78:1990_09
  • 79:1990_10
  • 80:1990_11
  • 81:1990_12
  • 82:1991_01
  • 83:1991_02
  • 84:1991_03
  • 85:1991_04
  • 86:1991_05
  • 87:1991_06
  • 88:1991_07
  • 89:1991_08
  • 90:1991_09
  • 91:1991_10
  • 92:1991_11
  • 93:1991_12
  • 94:1992_01
  • 95:1992_02
  • 96:1992_03
  • 97:1992_04
  • 98:1992_05
  • 99:1992_06
  • 100:1992_07
  • 101:1992_08
  • 102:1992_09
  • 103:1992_10
  • 104:1992_11
  • 105:1992_12
  • 106:1993_01
  • 107:1993_02
  • 108:1993_03
  • 109:1993_04
  • 110:1993_05
  • 111:1993_06
  • 112:1993_07
  • 113:1993_08
  • 114:1993_09
  • 115:1993_10
  • 116:1993_11
  • 117:1993_12
  • 118:1994_01
  • 119:1994_02
  • 120:1994_03
  • 121:1994_04
  • 122:1994_05
  • 123:1994_06
  • 124:1994_07
  • 125:1994_08
  • 126:1994_09
  • 127:1994_10
  • 128:1994_11
  • 129:1994_12
  • 130:1995_01
  • 131:1995_02
  • 132:1995_03
  • 133:1995_04
  • 134:1995_05
  • 135:1995_06
  • 136:1995_07
  • 137:1995_08
  • 138:1995_09
  • 139:1995_10
  • 140:1995_11
  • 141:1995_12
  • 142:1996_01
  • 143:1996_02
  • 144:1996_03
  • 145:1996_04
  • 146:1996_05
  • 147:1996_06
  • 148:1996_07
  • 149:1996_08
  • 150:1996_09
  • 151:1996_10
  • 152:1996_11
  • 153:1996_12
  • 154:1997_01
  • 155:1997_02
  • 156:1997_03
  • 157:1997_04
  • 158:1997_05
  • 159:1997_06
  • 160:1997_07
  • 161:1997_08
  • 162:1997_09
  • 163:1997_10
  • 164:1997_11
  • 165:1997_12
  • 166:1998_01
  • 167:1998_02
  • 168:1998_03
  • 169:1998_04
  • 170:1998_05
  • 171:1998_06
  • 172:1998_07
  • 173:1998_08
  • 174:1998_09
  • 175:1998_10
  • 176:1998_11
  • 177:1998_12
  • 178:1999_01
  • 179:1999_02
  • 180:1999_03
  • 181:1999_04
  • 182:1999_05
  • 183:1999_06
  • 184:1999_07
  • 185:1999_08
  • 186:1999_09
  • 187:1999_10
  • 188:1999_11
  • 189:1999_12
  • 190:2000_01
  • 191:2000_02
  • 192:2000_03
  • 193:2000_04
  • 194:2000_05
  • 195:2000_06
  • 196:2000_07
  • 197:2000_08
  • 198:2000_09
  • 199:2000_10
  • 200:2000_11
  • 201:2000_12
  • 202:2001_01
  • 203:2001_02
  • 204:2001_03
  • 205:2001_04
  • 206:2001_05
  • 207:2001_06
  • 208:2001_07
  • 209:2001_08
  • 210:2001_09
  • 211:2001_10
  • 212:2001_11
  • 213:2001_12
  • 214:2002_01
  • 215:2002_02
  • 216:2002_03
  • 217:2002_04
  • 218:2002_05
  • 219:2002_06
  • 220:2002_07
  • 221:2002_08
  • 222:2002_09
  • 223:2002_10
  • 224:2002_11
  • 225:2002_12
  • 226:2003_01
  • 227:2003_02
  • 228:2003_03
  • 229:2003_04
  • 230:2003_05
  • 231:2003_06
  • 232:2003_07
  • 233:2003_08
  • 234:2003_09
  • 235:2003_10
  • 236:2003_11
  • 237:2003_12
  • 238:2004_01
  • 239:2004_02
  • 240:2004_03
  • 241:2004_04
  • 242:2004_05
  • 243:2004_06
  • 244:2004_07
  • 245:2004_08
  • 246:2004_09
  • 247:2004_10
  • 248:2004_11
  • 249:2004_12
  • 250:2005_01
  • 251:2005_02
  • 252:2005_03
  • 253:2005_04
  • 254:2005_05
  • 255:2005_06
  • 256:2005_07
  • 257:2005_08
  • 258:2005_09
  • 259:2005_10
  • 260:2005_11
  • 261:2005_12
  • 262:2006_01
  • 263:2006_02
  • 264:2006_03
  • 265:2006_04
  • 266:2006_05
  • 267:2006_06
  • 268:2006_07
  • 269:2006_08
  • 270:2006_09
  • 271:2006_10
  • 272:2006_11
  • 273:2006_12
  • 274:2007_01
  • 275:2007_02
  • 276:2007_03
  • 277:2007_04
  • 278:2007_05
  • 279:2007_06
  • 280:2007_07
  • 281:2007_08
  • 282:2007_09
  • 283:2007_10
  • 284:2007_11
  • 285:2007_12
  • 286:2008_01
  • 287:2008_02
  • 288:2008_03
  • 289:2008_04
  • 290:2008_05
  • 291:2008_06
  • 292:2008_07
  • 293:2008_08
  • 294:2008_09
  • 295:2008_10
  • 296:2008_11
  • 297:2008_12
  • 298:2009_01
  • 299:2009_02
  • 300:2009_03
  • 301:2009_04
  • 302:2009_05
  • 303:2009_06
  • 304:2009_07
  • 305:2009_08
  • 306:2009_09
  • 307:2009_10
  • 308:2009_11
  • 309:2009_12
  • 310:2010_01
  • 311:2010_02
  • 312:2010_03
  • 313:2010_04
  • 314:2010_05
  • 315:2010_06
  • 316:2010_07
  • 317:2010_08
  • 318:2010_09
  • 319:2010_10
  • 320:2010_11
  • 321:2010_12
  • 322:2011_01
  • 323:2011_02
  • 324:2011_03
  • 325:2011_04
  • 326:2011_05
  • 327:2011_06
  • 328:2011_07
  • 329:2011_08
  • 330:2011_09
  • 331:2011_10
  • 332:2011_11
  • 333:2011_12
  • 334:2012_01
  • 335:2012_02
  • 336:2012_03
  • 337:2012_04
  • 338:2012_05
  • 339:2012_06
  • 340:2012_07
  • 341:2012_08
  • 342:2012_09
  • 343:2012_10
  • 344:2012_11
  • 345:2012_12
  • 346:2013_01
  • 347:2013_02
  • 348:2013_03
  • 349:2013_04
  • 350:2013_05
  • 351:2013_06
  • 352:2013_07
  • 353:2013_08
  • 354:2013_09
  • 355:2013_10
  • 356:2013_11
  • 357:2013_12
  • 358:2014_01
  • 359:2014_02
  • 360:2014_03
  • 361:2014_04
  • 362:2014_05
  • 363:2014_06
  • 364:2014_07
  • 365:2014_08
  • 366:2014_09
  • 367:2014_10
  • 368:2014_11
  • 369:2014_12
  • 370:2015_01
  • 371:2015_02
  • 372:2015_03
  • 373:2015_04
  • 374:2015_05
  • 375:2015_06
  • 376:2015_07
  • 377:2015_08
  • 378:2015_09
  • 379:2015_10
  • 380:2015_11
  • 381:2015_12
  • 382:2016_01
  • 383:2016_02
  • 384:2016_03
  • 385:2016_04
  • 386:2016_05
  • 387:2016_06
  • 388:2016_07
  • 389:2016_08
  • 390:2016_09
  • 391:2016_10
  • 392:2016_11
  • 393:2016_12
  • 394:2017_01
  • 395:2017_02
  • 396:2017_03
  • 397:2017_04
  • 398:2017_05
  • 399:2017_06
  • 400:2017_07
  • 401:2017_08
  • 402:2017_09
  • 403:2017_10
  • 404:2017_11
  • 405:2017_12
  • 406:2018_01
  • 407:2018_02
  • 408:2018_03
  • 409:2018_04
  • 410:2018_05
  • 411:2018_06
  • 412:2018_07
  • 413:2018_08
  • 414:2018_09
  • 415:2018_10
  • 416:2018_11
  • 417:2018_12
  • 418:2019_01
  • 419:2019_02
  • 420:2019_03
  • 421:2019_04
  • 422:2019_05
  • 423:2019_06
  • 424:2019_07
  • 425:2019_08
  • 426:2019_09
  • 427:2019_10
  • 428:2019_11
  • 429:2019_12
  • 430:2020_01
  • 431:2020_02
  • 432:2020_03
  • 433:2020_04
  • 434:2020_05
  • 435:2020_06
  • 436:2020_07
  • 437:2020_08
  • 438:2020_09
  • 439:2020_10
  • 440:2020_11
  • 441:2020_12
  • 442:2021_01
  • 443:2021_02
  • 444:2021_03
  • 445:2021_04
  • 446:2021_05
  • 447:2021_06
  • 448:2021_07
  • 449:2021_08
  • 450:2021_09
  • 451:2021_10
  • 452:2021_11
  • 453:2021_12

In [85]:

Map = geemap.Map()
Map

Map = geemap.Map() Map

Out[85]:

In [86]:

image = dataset.filterDate('2020-08-01', '2020-09-01').first()
region = Map.user_roi # Draw a polygon on the map
if region is None:
    region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531)
vis_params = {'min': 0.0, 'max': 2.0, 'palette': ['ffffff', 'fffcb8', '0905ff']}

Map.addLayer(image, vis_params, 'Water')
Map.addLayer(region, {}, 'ROI', True, 0.5)
Map.centerObject(region)

image = dataset.filterDate('2020-08-01', '2020-09-01').first() region = Map.user_roi # Draw a polygon on the map if region is None: region = ee.Geometry.BBox(-99.957, 46.8947, -99.278, 47.1531) vis_params = {'min': 0.0, 'max': 2.0, 'palette': ['ffffff', 'fffcb8', '0905ff']} Map.addLayer(image, vis_params, 'Water') Map.addLayer(region, {}, 'ROI', True, 0.5) Map.centerObject(region)

In [87]:

geemap.jrc_hist_monthly_history(
    region=region, scale=30, frequency='month', denominator=1e4, y_label='Area (ha)'
)

geemap.jrc_hist_monthly_history( region=region, scale=30, frequency='month', denominator=1e4, y_label='Area (ha)' )

In [88]:

geemap.jrc_hist_monthly_history(
    region=region,
    start_month=6,
    end_month=9,
    scale=30,
    frequency='month',
    denominator=1e4,
    y_label='Area (ha)',
)

geemap.jrc_hist_monthly_history( region=region, start_month=6, end_month=9, scale=30, frequency='month', denominator=1e4, y_label='Area (ha)', )

In [89]:

geemap.jrc_hist_monthly_history(
    region=region,
    start_month=6,
    end_month=9,
    scale=30,
    frequency='year',
    reducer='mean',
    denominator=1e4,
    y_label='Area (ha)',
)

geemap.jrc_hist_monthly_history( region=region, start_month=6, end_month=9, scale=30, frequency='year', reducer='mean', denominator=1e4, y_label='Area (ha)', )

/Users/qswu/mambaforge/envs/geo/lib/python3.9/site-packages/geemap/common.py:13302: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.

In [90]:

geemap.jrc_hist_monthly_history(
    region=region,
    start_month=6,
    end_month=9,
    scale=30,
    frequency='year',
    reducer='max',
    denominator=1e4,
    y_label='Area (ha)',
)

geemap.jrc_hist_monthly_history( region=region, start_month=6, end_month=9, scale=30, frequency='year', reducer='max', denominator=1e4, y_label='Area (ha)', )

---

Last update: 2023-09-13